2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
This paper presents a Deep Reinforcement Learning based navigation approach in which we define th... more This paper presents a Deep Reinforcement Learning based navigation approach in which we define the occupancy observations as heuristic evaluations of motion primitives, rather than using raw sensor data. Our method enables fast mapping of the occupancy data, generated by multi-sensor fusion, into trajectory values in 3D workspace. The computationally efficient trajectory evaluation allows dense sampling of the action space. We utilize our occupancy observations in different data structures to analyze their effects on both training process and navigation performance. We train and test our methodology on two different robots within challenging physics-based simulation environments including static and dynamic obstacles. We benchmark our occupancy representations with other conventional data structures from state-of-the-art methods. The trained navigation policies are also validated successfully with physical robots in dynamic environments. The results show that our method not only decreases the required training time but also improves the navigation performance as compared to other occupancy representations. The open-source implementation of our work and all related info are available at https://github.com/RIVeR-Lab/tentabot.
This paper presents a Deep Reinforcement Learning based navigation approach in which we define th... more This paper presents a Deep Reinforcement Learning based navigation approach in which we define the occupancy observations as heuristic evaluations of motion primitives, rather than using raw sensor data. Our method enables fast mapping of the occupancy data, generated by multi-sensor fusion, into trajectory values in 3D workspace. The computationally efficient trajectory evaluation allows dense sampling of the action space. We utilize our occupancy observations in different data structures to analyze their effects on both training process and navigation performance. We train and test our methodology on two different robots within challenging physics-based simulation environments including static and dynamic obstacles. We benchmark our occupancy representations with other conventional data structures from state-of-the-art methods. The trained navigation policies are also validated successfully with physical robots in dynamic environments. The results show that our method not only decreases the required training time but also improves the navigation performance as compared to other occupancy representations. The open-source implementation of our work and all related info are available at https://github.com/RIVeR-Lab/tentabot.
Cluttered environments with partial object occlusions pose significant challenges to robot manipu... more Cluttered environments with partial object occlusions pose significant challenges to robot manipulation. In settings composed of one dominant object type and various undesirable contaminants, occlusions make it difficult to both recognize and isolate undesirable objects. Spatial features alone are not always sufficiently distinct to reliably identify anomalies under multiple layers of clutter, with only a fractional part of the object exposed. We create a multi-modal data representation of cluttered object scenes pairing depth data with a registered hyperspectral data cube. Hyperspectral imaging provides pixel-wise Visible Near-Infrared (VNIR) reflectance spectral curves which are invariant in similar material types. Spectral reflectance data is grounded in the chemical-physical properties of an object, making spectral curves an excellent modality to differentiate inter-class material types. Our approach proposes a new automated method to perform hyperspectral anomaly detection in c...
2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
This paper presents a Deep Reinforcement Learning based navigation approach in which we define th... more This paper presents a Deep Reinforcement Learning based navigation approach in which we define the occupancy observations as heuristic evaluations of motion primitives, rather than using raw sensor data. Our method enables fast mapping of the occupancy data, generated by multi-sensor fusion, into trajectory values in 3D workspace. The computationally efficient trajectory evaluation allows dense sampling of the action space. We utilize our occupancy observations in different data structures to analyze their effects on both training process and navigation performance. We train and test our methodology on two different robots within challenging physics-based simulation environments including static and dynamic obstacles. We benchmark our occupancy representations with other conventional data structures from state-of-the-art methods. The trained navigation policies are also validated successfully with physical robots in dynamic environments. The results show that our method not only decreases the required training time but also improves the navigation performance as compared to other occupancy representations. The open-source implementation of our work and all related info are available at https://github.com/RIVeR-Lab/tentabot.
This paper presents a Deep Reinforcement Learning based navigation approach in which we define th... more This paper presents a Deep Reinforcement Learning based navigation approach in which we define the occupancy observations as heuristic evaluations of motion primitives, rather than using raw sensor data. Our method enables fast mapping of the occupancy data, generated by multi-sensor fusion, into trajectory values in 3D workspace. The computationally efficient trajectory evaluation allows dense sampling of the action space. We utilize our occupancy observations in different data structures to analyze their effects on both training process and navigation performance. We train and test our methodology on two different robots within challenging physics-based simulation environments including static and dynamic obstacles. We benchmark our occupancy representations with other conventional data structures from state-of-the-art methods. The trained navigation policies are also validated successfully with physical robots in dynamic environments. The results show that our method not only decreases the required training time but also improves the navigation performance as compared to other occupancy representations. The open-source implementation of our work and all related info are available at https://github.com/RIVeR-Lab/tentabot.
Cluttered environments with partial object occlusions pose significant challenges to robot manipu... more Cluttered environments with partial object occlusions pose significant challenges to robot manipulation. In settings composed of one dominant object type and various undesirable contaminants, occlusions make it difficult to both recognize and isolate undesirable objects. Spatial features alone are not always sufficiently distinct to reliably identify anomalies under multiple layers of clutter, with only a fractional part of the object exposed. We create a multi-modal data representation of cluttered object scenes pairing depth data with a registered hyperspectral data cube. Hyperspectral imaging provides pixel-wise Visible Near-Infrared (VNIR) reflectance spectral curves which are invariant in similar material types. Spectral reflectance data is grounded in the chemical-physical properties of an object, making spectral curves an excellent modality to differentiate inter-class material types. Our approach proposes a new automated method to perform hyperspectral anomaly detection in c...
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Papers by Gary Lvov